Abstract
Background: Electricity is considered as the essential unit in today’s high-tech world. The electricity demand has been increased very rapidly due to increased urbanization,(smart buildings, and usage of smart devices to a large extent). Building a reliable and accurate electricity consumption prediction model becomes necessary with the increase in demand for energy. From recent studies, prediction models such as support vector regression (SVR), gradient boosting decision tree (GBDT), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost) have been compared for the prediction of electricity consumption and XGBoost is found to be the most efficient method that leads to the motivation for the research.
Objective: The objective of this research is to propose a model that performs future electricity consumption prediction for different time horizons: short term prediction and long term prediction using the extreme gradient boosting method and reduce prediction errors. Also, based on the prediction of the electricity consumption, the best and worst predicted days are being recognized.
Methods: The method used in this research is the extreme gradient boosting for future building electricity consumption prediction. The extreme gradient boosting method performs predictions for different time horizons(short term and long term) for different seasons(summer and winter). The model was designed for a house building located in Paris.
Results: The model has been trained and tested on the dataset and its prediction is accurate with the low rate of errors compared to other machine learning techniques. The model predicts accurately with RMSE of 140.45 and MAE of 28, which is the least value for errors when compared to the baseline prediction models.
Conclusion: A model that is robust to all the conditions should be built by enhancing the prediction mechanism such that the model should be dependent on a few factors to make electricity consumption prediction.
Keywords: Building electricity consumption prediction, extreme gradient boosting, ensemble techniques, short-term prediction, long-term prediction, machine learning techniques.
Graphical Abstract
[2]
"Gartner", Gartner Survey Shows Connected Home Solutions Adoption Remains Limited to Early adopters, 2017.
[6]
A.S. Shah, H. Nasir, M. Fayaz, A. Lajos, and A. Shah, "A review on energy consumption optimization techniques in IoT based smart building environments", Information, vol. 10, p. 108, 2019.
[http://dx.doi.org/10.3390/info10030108]
[http://dx.doi.org/10.3390/info10030108]
[7]
F. Wahid, R. Ghazali, M. Fayaz, and A.S. Shah, "Statistical features based approach (sfba) for hourly energy consumption prediction using neural network", Int. J. Inf. Technol. Comput. Sci., vol. 9, pp. 23-30, 2017.
[http://dx.doi.org/10.5815/ijitcs.2017.05.04]
[http://dx.doi.org/10.5815/ijitcs.2017.05.04]
[8]
F. Wahid, R. Ghazali, M. Fayaz, and A.S. Shah, "A simple and easy approach for home appliances energy consumption prediction in residential buildings using machine learning techniques", JAEBS, vol. 7, pp. 108-119, 2017.
[9]
M. Fayaz, and D. Kim, "A prediction methodology of energy consumption based on deep extreme learning machine and comparative analysis in residential buildings", Electronics (Basel), vol. 7, p. 222, 2018.
[http://dx.doi.org/10.3390/electronics7100222]
[http://dx.doi.org/10.3390/electronics7100222]
[10]
G.E. Atlanta, "Thermal Environmental Conditions for Human Occupancy. American Society of Heating, Refrigerating, and Air Conditioning Engineers",
[11]
J. Stinson, A. Willis, J.B. Williamson, J. Currie, and R.S. Smith, "Visualising energy use for smart homes and informed users", Energy Procedia, vol. 78, pp. 579-584, 2015.
[http://dx.doi.org/10.1016/j.egypro.2015.11.015]
[http://dx.doi.org/10.1016/j.egypro.2015.11.015]
[12]
D.L. Ha, S. Ploix, E. Zamai, and M. Jacomino, "Real-time dynamic optimization for demand-side load management", IJMSEM, vol. 3, pp. 243-252, 2008.
[13]
K. Li, C. Hu, G. Liu, and W. Xue, "Building’s electricity consumption prediction using optimized artificial neural networks and principal component analysis", Energy Build., vol. 108, pp. 106-113, 2015.
[http://dx.doi.org/10.1016/j.enbuild.2015.09.002]
[http://dx.doi.org/10.1016/j.enbuild.2015.09.002]
[14]
Z. Wang, and S. Ravi, "A review of artificial intelligence-based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models", Renewable and Sustainable Energy Reviews, vol. 75, pp. 796-808, 2017.
[15]
Y.T. Chae, R. Horesh, Y. Hwang, and Y.M. Lee, "Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings", Energy and Buildings, vol. 111, pp. 184-194, 2016.
[16]
G. Jorjeta, "Neural network model ensembles for building-level electricity load forecasts", Energy and Buildings, vol. 84, pp. 214-223, 2014.
[17]
Jung Hyun Chul, Kim Jin-Sung, and HoonHeo, "Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach, Energy and Buildings",
[18]
Chitsaz Hamed, Shaker Hamid, Zareipour Hamidreza, Wood David, and Amjady Nima, "Short-term electricity load forecasting of buildings in microgrids", Energy and Buildings, vol. 99, pp. 50-60, 2015.
[19]
Escrivá-Escrivá Guillermo, Álvarez-Bel Carlos, Roldán-Blay Carlos, and Alcázar-Ortega Manuel, "New artificial neural network prediction method for electrical consumption forecasting based on building end-uses", Energy and Buildings, vol. 43, no. 11, pp. 3112-3119, 2011.
[20]
E. Richard, "Predicting future hourly residential electrical consumption: A machine learning case study", Energy and Buildings, vol. 49, pp. 591-603, 2012.
[21]
A. Yezioro, B. Dong, and F. Leite, "An applied artificial intelligence approach towards assessing building performance simulation tools", Energy and Buildings, vol. 40, no. 4, pp. 612-620, 2008.
[22]
Zhong Hai, Wang Jiajun, Jia Hongjie, Mu Yunfei, and ShileiLv, "Vector field-based support vector regression for building energy consumption prediction", Applied Energy, vol. 242, pp. 403-414, 2019.
[23]
M.C. Leung, and C.F. Norman, "The use of occupancy space electrical power demand in building cooling load prediction", Energy and Buildings, vol. 55, pp. 151-163, 2012.
[24]
C. Fan, F. Xiao, and S. Wang, "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques", Applied Energy, vol. 127, pp. 1-10, 2014.
[25]
L. Wang, and W.M. Eric, "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach", Applied Energy, vol. 228, pp. 1740-1753, 2018.
[26]
E. Abdullatif, "Cooling load prediction for buildings using general regression neural networks", Energy Conversion and Management, vol. 45, no. 13-14, pp. 2127-2141, 2004.
[27]
Q. Li, "Applying support vector machine to predict hourly cooling load in the building", Applied Energy, vol. 86, no. 10, pp. 2249-2256, 2009.
[28]
G. Hebrail, and A. Berard, "Individual Household Electric Power Consumption Data Set", UCI Machine Learning Repository.
[29]
K. Amasyali, and N.M. El-Gohary, "A review of data-driven building energy consumption prediction studies", Renewable and Sustainable Energy Reviews, vol. 81, pp. 1192-1205, 2018.
[30]
H-X. Zhao, "A review on the prediction of building energy consumption", Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 3586-3592, 2012.
[31]
G.T. Heinemann, D.A. Nordman, and E.C. Plant, "The Relationship Between Summer Weather and Summer Loads - A Regression Analysis", IEEE Trans. Power Apparatus Syst, vol. PAS-85, no. 11, pp. 1144-1154, 1966.
[http://dx.doi.org/10.1109/TPAS.1966.291535]
[http://dx.doi.org/10.1109/TPAS.1966.291535]
[32]
F. Apadula, A. Bassini, A. Elli, and S. Scapin, "Relationships between meteorological variables and monthly electricity demand", Applied Energy, vol. 98, pp. 346-356, 2012.
[33]
W.R. Christiaanse, "Short-Term Load Forecasting Using General Exponential Smoothing", IEEE Trans. Power Apparatus Syst, vol. PAS-90, no. 2, pp. 900-911, 1971.
[http://dx.doi.org/10.1109/TPAS.1971.293123]
[http://dx.doi.org/10.1109/TPAS.1971.293123]
[34]
N. Amjady, "Short-term hourly load forecasting using time-series modeling with peak load estimation capability", IEEE Trans. Power Syst., vol. 16, pp. 498-505, 2001.
[http://dx.doi.org/10.1109/59.932287]
[http://dx.doi.org/10.1109/59.932287]
[35]
R. Becker, and D. Thrän, "Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighbors", Applied Energy, vol. 208, pp. 252-262, 2017.
[36]
M. Eric, "Gated ensemble learning method for demand-side electricity load forecasting", Energy and Buildings, vol. 109, pp. 23-24, 2015.
[37]
H. Long, Z. Zhang, and Y. Su, "Analysis of daily solar power prediction with data-driven approaches", Applied Energy, vol. 126, pp. 29-37, 2014.
[38]
T. Chen, and C. Guestrin, "XGBoost: A scalable tree boosting system", ACM SIGKDD International Conference on knowledge discovery and data mining, 2016.
[http://dx.doi.org/10.1145/2939672.2939785]
[http://dx.doi.org/10.1145/2939672.2939785]
[39]
A.S. Ahmad, M.Y. Hassan, M.P. Abdullah, H.A. Rahman, F. Hussin, H. Abdullah, and R. Saidur, "A review on applications of ANN and SVM for building electrical energy consumption forecasting", Renewable and Sustainable Energy Reviews, vol. 33, pp. 102-109, 2014.
[40]
Y. Chen, "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings", Applied Energy, vol. 195, pp. 659-670, 2017.
[41]
B. Dong, and C. Cao, "Applying support vector machines to predict building energy consumption in a tropical region, Energy and Buildings",
[42]
M. Fayaz, H. Shah, A.M. Aseere, W.K. Mashwani, and A.S. Shah, "A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network", Technologies, vol. 7, no. 2, 2019.
[43]
M. Muller, "Creating building energy prediction models with convolutional recurrent neural networks",
[44]
T. Ahmad, and H. Chen, "Deep learning for multi-scale smart energy forecasting", Energy, 2019.
[http://dx.doi.org/10.1016/j.energy.2019.03.080]
[http://dx.doi.org/10.1016/j.energy.2019.03.080]
[45]
X. Yuan, J. Zhou, B. Huang, Y. Wang, C. Yang, and W. Gui, "Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy", IEEE Trans. Industr. Inform., vol. 16, no. 6, pp. 3721-3730, 2020.
[http://dx.doi.org/10.1109/TII.2019.2938890]
[http://dx.doi.org/10.1109/TII.2019.2938890]
[46]
Reid SJDoCS, "the University of Colorado at Boulder", A review of heterogeneous ensemble methods, 2007.
[47]
Kadir Amasyali, "Predicting Energy Consumption of Office Buildings: A Hybrid Machine Learning-Based Approach", Advances in Informatics and Computing in Civil and Construction Engineering Springer International Publishing.
[48]
Nemshan Alharthi, and Adnan Gutub, "Data Visualization to Explore Improving Decision-Making within Hajj Services",
[http://dx.doi.org/10.20448/808.2.1.9.18]
[http://dx.doi.org/10.20448/808.2.1.9.18]
[49]
A. Sharif, and A. Hammad, "Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC, and LCA", J. Build. Eng., vol. 25, 2019.100790
[http://dx.doi.org/10.1016/j.jobe.2019.100790]
[http://dx.doi.org/10.1016/j.jobe.2019.100790]
[50]
C. Fan, Y. Sun, Y. Zhao, M. Song, and J. Wang, "Deep learning-based feature engineering methods for improved building energy prediction", Applied Energy, vol. 240, pp. 35-45, 2019.
[51]
M. Kim, W. Choi, Y. Jeon, and L. Liu, "A Hybrid Neural Network Model for Power Demand Forecasting", Energies, vol. 12, p. 931, 2019.
[http://dx.doi.org/10.3390/en12050931]
[http://dx.doi.org/10.3390/en12050931]
[52]
M. Torabi, S. Hashemi, M.R. Saybani, S. Shamshirband, and A. Mosavi, "A Hybrid clustering and classification technique for forecasting short‐term energy consumption", Environ. Prog. Sustain. Energy, vol. 38, pp. 66-76, 2019.
[http://dx.doi.org/10.1002/ep.12934]
[http://dx.doi.org/10.1002/ep.12934]
[53]
Wang Wei, Hong Tianzhen, and Chen Jiayu, "Incorporating machine learning with building network analysis to predict multi-building energy use", Energy & Buildings, 2019.
[54]
C. Fan, J. Wang, W. Gang, and S. Li, "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions", Applied Energy, vol. 236, pp. 700-710, 2019.
[55]
Jatin Bedi, "Deep learning framework to forecast electricity demand", Applied Energy, vol. 238, pp. 1312-1326, 2019.
[56]
F. Divina, and G. Torres, "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings", Energies, vol. 12, 1934.
[http://dx.doi.org/10.3390/en12101934]
[http://dx.doi.org/10.3390/en12101934]
[57]
N.J. Johannesen, M. Kolhe, and M. Goodwin, "Relative evaluation of regression tools for urban area electrical energy demand forecasting", J. Clean. Prod., 2019.
[http://dx.doi.org/10.1016/j.jclepro.2019.01.108]
[http://dx.doi.org/10.1016/j.jclepro.2019.01.108]
[58]
DebadityaChakraborty&HazemElzarka, "Advanced machine learning techniques for building performance simulation: a comparative analysis", J. Build. Perform. Simul., 2018.
[http://dx.doi.org/10.1080/19401493.2018.1498538]
[http://dx.doi.org/10.1080/19401493.2018.1498538]
[59]
Sangireddy SaiAbhilash Reddy, Bhatia Aviruch, and Garg Vishal, "Development Of A Surrogate Model By Extracting Top Characteristic Feature Vectors For Building Energy Prediction", J. Build. Eng..
[http://dx.doi.org/10.1016/j.jobe.2018.12.018]
[http://dx.doi.org/10.1016/j.jobe.2018.12.018]
[60]
N. Farooqi, A. Gutub, and O. Khozium, "Smart Community Challenges: Enabling IoT/M2M Technology Case Study", Life Sci. J., vol. 16, pp. 11-17, 2019.
[http://dx.doi.org/10.7537/marslsj160719.03]
[http://dx.doi.org/10.7537/marslsj160719.03]
[61]
S.A. Aly, T.A. AlGhamdi, M. Salim, H.H. Amin, and A.A. Gutub, "Information Gathering Schemes For Collaborative Sensor Devices", Procedia Comput. Sci., vol. 32, pp. 1141-1146, 2014.
[http://dx.doi.org/10.1016/j.procs.2014.05.545]
[http://dx.doi.org/10.1016/j.procs.2014.05.545]
[62]
S.S. Roy, P. Samui, and I. Nagtode, "Forecasting heating and cooling loads of buildings: a comparative performance analysis", J. Ambient Intell. Humaniz. Comput., vol. 11, pp. 1253-1264, 2020.
[http://dx.doi.org/10.1007/s12652-019-01317-y]
[http://dx.doi.org/10.1007/s12652-019-01317-y]
[63]
A. Galicia, R. Talavera-Llames, A. Troncoso, I. Koprinska, and F. Martínez-Álvarez, "Multi-step forecasting for big data time series based on ensemble learning", Knowledge-Based Systems, vol. 163, pp. 830-841, 2019.
[64]
R. Wang, S. Lu, and X. Li, "Multi-criteria comprehensive study on a predictive algorithm of hourly heating energy consumption for residential buildings", Sustainable Cities and Society, vol. 49, 2019.101623
[65]
Ž. Radiša, "Ensemble of various neural networks for prediction of heating energy consumption", Energy and Buildings, vol. 94, pp. 189-199, 2015.
[66]
J. Fan, X. Wang, L. Wu, H. Zhou, F. Zhang, X. Yu, X. Lu, and Y. Xiang, Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China, Energy Conversion and Management.
[68]
T. Le, M.T. Vo, B. Vo, E. Hwang, S. Rho, and S.W. Baik, "Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM", Appl. Sci. (Basel), vol. 9, p. 4237, 2019.
[http://dx.doi.org/10.3390/app9204237]
[http://dx.doi.org/10.3390/app9204237]
[69]
X. Yuan, C. Ou, Y. Wang, C. Yang, and W. Gui, "A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process", IEEE Trans. Neural Netw. Learn. Syst., 2019.
[http://dx.doi.org/10.1109/TNNLS.2019.2951708] [PMID: 31841424]
[http://dx.doi.org/10.1109/TNNLS.2019.2951708] [PMID: 31841424]
[70]
X. Yuan, L. Li, Y. Shardt, Y. Wang, and C. Yang, "Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development", IEEE Trans. Ind. Electron..
[http://dx.doi.org/10.1109/TIE.2020.2984443]
[http://dx.doi.org/10.1109/TIE.2020.2984443]
Related Journals
International Journal of Sensors, Wireless Communications and Control
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
Article Metrics
5